Chapter 2 – Cities as Labor Markets
Cities are primarily labor markets. Usually 35 to 50 percent of any urban population does not
participate directly in the labor market, this is the dependant population. Large cities generate
economies of scale that allow enterprises to reduce their costs by increasing output, thereby
reducing costs per unit. Economies of scale are only possible in cities with a large labor market.
Knowledge spillovers are responsible for agglomeration economies, economies that increase
productivity due to the rapid dissemination of new ideas because of large numbers of workers in
close contact. Agglomeration economies also result from a lowering of transaction costs in larger
cities because of the proximity of competing suppliers and consumers. Moving to a smaller city
where land is cheaper and salaries are lower makes economic sense for firms whose activities
require a lot of land and labor that is not particularly specialized.
A cities’ growth rates are random, with the same long-term average expected growth rate and same
variance. Productivity increases with city size only if the transportation network is able to connect
workers with firms and providers of goods and services with consumers. This connectivity is difficult
to achieve in large cities as it requires consistency among a number of factors: land use and
investments for transport networks; pricing decisions for road use, parking, and transit fares; and
collection of local taxes and user fees. A city function well if it offers:
a. Commutes short enough that one has time for leisure activities
b. An open job market that facilitates search for better job matches
c. For each worker a residence from which access to social life is quick and easy, the consumer
city is of growing importance.
Without a functioning labor market there is no city, exception are retiree cities, where income has
been generated earlier.
The Spatial Pattern of Labor Mobility
The larger the total number of jobs is, the greater will be the chances that a few highly specialized
jobs are among them. Workers’ mobility is a key factor in increasing the productivity of large cities
and the welfare of their workers.
Imagine a linear city, where workers’
residences are spread evenly between a
and e. Jobs are concentrated in only
three locations b, c and d. Each location
contains 1/3 of all jobs. The speed of
transport is uniform within the city and
is represented by the arrows showing
travel times between different points. It
takes 2 hours to travel from a to e, which
are on opposite outer edges of the hypothetical city. Workers living between b and d can reach 100
percent of the jobs in less than 1 hour, but workers living between a and b can reach only the jobs
located in b and c in less than 1 hour; jobs located in d are out of reach for workers living between a
and b. Similarly, workers living between d and e can reach only the jobs located in c and d; the jobs
located in b are out of reach. As a consequence, 50 percent of the workers have access to 100
percent of the jobs in less than 1 hour of travel time, while the other 50 percent only have access to
2/3 of all the jobs. Therefore, the effective size of the labor market represented is only 83 percent of
all the jobs available in the city. If the speed of transport could be increased so that one could travel
, from a to d and from e to b within 1 hour, rather than the 90 minutes each trip currently takes, then
the effective size of the job market would be 100 percent of all jobs available.
The effective size of a labor market depends on commuting travel speeds and the relative location of
workers’ residences to their jobs.
In this circle, smaller red circles
represent job locations. For each
pattern of job distribution, an arrow
shows the maximum travel distance
that a worker can cover in 1 hour
from the outer edge of the urban
area. The different arrow lengths
correspond to different travel
speeds. In a monocentric or
polycentric clustered area, workers
who live in a more central area may
have access to all jobs in the built-up
area, but workers living on the periphery have access to only a fraction of the total jobs available in
the city. In this case, the implied productivity of a large labor market is not fully realized.
A decrease in commuting travel speed fragments large labor markets into smaller ones and results in
a decrease in urban productivity. Increasing travel speed decreases the difference between the
effective labor market (the number of jobs accessible in an hour’s commute) and the nominal labor
market (the total number of jobs in a metropolitan area).
This figure illustrates the most typical trip
patterns in metropolitan areas. These trips are
based on the structure of the labor market.
a. Monocentric model: most jobs are
concentrated in a dense CBD. Trip
routes follow radial roads and
converge on the CBD. Of course, no real city is ever strictly monocentric, as a number of jobs
are necessarily found inside residential areas. A city where more than 50 percent of the jobs
are located in the CBD is predominantly monocentric.
b. Dispersed model: most jobs are concentrated in small clusters or completely dispersed
among residential areas. Trip routes are randomly distributed in the built-up area. If speed
of transport allows it, some workers will commute from one edge of the metropolitan area
to its opposite edge.
c. Composite model: a significant fraction of all jobs are concentrated in a dense CBD, but
most jobs are randomly distributed in the rest of the built-up area. Trip routes toward the
CBD follow radial roads, while trip routes toward dispersed jobs are randomly distributed
but usually avoid the congestion of the CBD. This is the most usual pattern of trips in large
cities in Asia and Europe today.
d. Urban village model: jobs are concentrated in many small clusters. In this model, there are
many centres, but commuters travel only to the center that is closest to their residence. The
trips toward each job cluster follow radial routes centered on each cluster and behave as if
Cities are primarily labor markets. Usually 35 to 50 percent of any urban population does not
participate directly in the labor market, this is the dependant population. Large cities generate
economies of scale that allow enterprises to reduce their costs by increasing output, thereby
reducing costs per unit. Economies of scale are only possible in cities with a large labor market.
Knowledge spillovers are responsible for agglomeration economies, economies that increase
productivity due to the rapid dissemination of new ideas because of large numbers of workers in
close contact. Agglomeration economies also result from a lowering of transaction costs in larger
cities because of the proximity of competing suppliers and consumers. Moving to a smaller city
where land is cheaper and salaries are lower makes economic sense for firms whose activities
require a lot of land and labor that is not particularly specialized.
A cities’ growth rates are random, with the same long-term average expected growth rate and same
variance. Productivity increases with city size only if the transportation network is able to connect
workers with firms and providers of goods and services with consumers. This connectivity is difficult
to achieve in large cities as it requires consistency among a number of factors: land use and
investments for transport networks; pricing decisions for road use, parking, and transit fares; and
collection of local taxes and user fees. A city function well if it offers:
a. Commutes short enough that one has time for leisure activities
b. An open job market that facilitates search for better job matches
c. For each worker a residence from which access to social life is quick and easy, the consumer
city is of growing importance.
Without a functioning labor market there is no city, exception are retiree cities, where income has
been generated earlier.
The Spatial Pattern of Labor Mobility
The larger the total number of jobs is, the greater will be the chances that a few highly specialized
jobs are among them. Workers’ mobility is a key factor in increasing the productivity of large cities
and the welfare of their workers.
Imagine a linear city, where workers’
residences are spread evenly between a
and e. Jobs are concentrated in only
three locations b, c and d. Each location
contains 1/3 of all jobs. The speed of
transport is uniform within the city and
is represented by the arrows showing
travel times between different points. It
takes 2 hours to travel from a to e, which
are on opposite outer edges of the hypothetical city. Workers living between b and d can reach 100
percent of the jobs in less than 1 hour, but workers living between a and b can reach only the jobs
located in b and c in less than 1 hour; jobs located in d are out of reach for workers living between a
and b. Similarly, workers living between d and e can reach only the jobs located in c and d; the jobs
located in b are out of reach. As a consequence, 50 percent of the workers have access to 100
percent of the jobs in less than 1 hour of travel time, while the other 50 percent only have access to
2/3 of all the jobs. Therefore, the effective size of the labor market represented is only 83 percent of
all the jobs available in the city. If the speed of transport could be increased so that one could travel
, from a to d and from e to b within 1 hour, rather than the 90 minutes each trip currently takes, then
the effective size of the job market would be 100 percent of all jobs available.
The effective size of a labor market depends on commuting travel speeds and the relative location of
workers’ residences to their jobs.
In this circle, smaller red circles
represent job locations. For each
pattern of job distribution, an arrow
shows the maximum travel distance
that a worker can cover in 1 hour
from the outer edge of the urban
area. The different arrow lengths
correspond to different travel
speeds. In a monocentric or
polycentric clustered area, workers
who live in a more central area may
have access to all jobs in the built-up
area, but workers living on the periphery have access to only a fraction of the total jobs available in
the city. In this case, the implied productivity of a large labor market is not fully realized.
A decrease in commuting travel speed fragments large labor markets into smaller ones and results in
a decrease in urban productivity. Increasing travel speed decreases the difference between the
effective labor market (the number of jobs accessible in an hour’s commute) and the nominal labor
market (the total number of jobs in a metropolitan area).
This figure illustrates the most typical trip
patterns in metropolitan areas. These trips are
based on the structure of the labor market.
a. Monocentric model: most jobs are
concentrated in a dense CBD. Trip
routes follow radial roads and
converge on the CBD. Of course, no real city is ever strictly monocentric, as a number of jobs
are necessarily found inside residential areas. A city where more than 50 percent of the jobs
are located in the CBD is predominantly monocentric.
b. Dispersed model: most jobs are concentrated in small clusters or completely dispersed
among residential areas. Trip routes are randomly distributed in the built-up area. If speed
of transport allows it, some workers will commute from one edge of the metropolitan area
to its opposite edge.
c. Composite model: a significant fraction of all jobs are concentrated in a dense CBD, but
most jobs are randomly distributed in the rest of the built-up area. Trip routes toward the
CBD follow radial roads, while trip routes toward dispersed jobs are randomly distributed
but usually avoid the congestion of the CBD. This is the most usual pattern of trips in large
cities in Asia and Europe today.
d. Urban village model: jobs are concentrated in many small clusters. In this model, there are
many centres, but commuters travel only to the center that is closest to their residence. The
trips toward each job cluster follow radial routes centered on each cluster and behave as if
